Online Master’s in Data Analytics Curriculum
Online Master’s in Data Analytics Curriculum
Online Master’s in Data Analytics Curriculum

Maryville University Online Master’s in Data Analytics Curriculum

The online Master of Science in Data Analytics at Maryville University aims to prepare students for professional success in the field of data analytics.

You can complete your Maryville University online Master of Science in Data Analytics in just 30 credit hours. Start at the right time for you by choosing from six admission points throughout the year. Explore the curriculum below to see how you can complete your master’s in data analytics in as little as one year of full-time or two years of part-time study.

Foundational Courses

Admission Prerequisite: BUS-501, Survey of Business, will be required if your GPA is below a 3.0 and/or if your undergraduate degree was outside the area of business; however, credits earned in foundational courses (such as BUS 241 and BUS 501) are considered prerequisites to courses required for the graduate degree.

  • Data analytics is generally defined as the use of data, quantitative analysis, and modeling to drive business decisions. This course serves as an introduction to data analytics and the key analytical techniques used for business decision-making. This introductory course aims to provide an overview of programming and data analytics software to develop your foundational programming skills. The course covers basic principles and practical issues combined with hands-on projects that effectively integrate analytics topics using various software.

  • This course examines database management applications to design, develop, and manage relational databases and data resources. The course also covers the integration of these databases with applications across the enterprise with a specific application towards business intelligence. Topics include the relational database model, requirements gathering, entity-relationship modeling, architecture, normalization, design for data warehousing, and extracting, transforming, and loading strategies to support business intelligence applications through a hands-on project. Corequisite: DATA 600

  • This course examines data mining techniques to explore patterns or relationships in data using methods such as classification, regression, cluster analysis, and recommendation systems. This course covers the analysis of both structured and unstructured data. Topics include data preparation, modeling, evaluation, and application. Widely adopted data mining software tools will be employed through a project-based learning approach to detecting patterns. Prerequisite: DATA 600

  • This course discusses techniques for developing effective dashboards to facilitate data-driven business decision-making. The course focuses on creating visualizations to communicate patterns and relationships in data effectively. Several software applications will be employed to enable storytelling through project-based learning. Prerequisite: DATA 600

  • This course covers the concept of predictive analytics, which combines business strategy, information technology, and modeling methods. It focuses on appropriate data selection, mathematical and/or statistical method selection, reporting, and visualization to assist business leaders in decision-making. Predictive models will be created using a hands-on approach of project-based learning. Prerequisite: DATA 620

  • This course is the final course of the data analytics program. It allows you to demonstrate and integrate the skills learned in the program in a final capstone project experience. You should plan to take this course in the last term of your studies. Prerequisite: Taken as last course of the program

Electives (12 credits)

Students may select elective credit hours from graduate coursework offered at Maryville University in accounting, business, communication, cybersecurity, finance, health administration, human resources management, marketing, and software development. Students may opt to complete a certificate from the options listed below. Additional coursework may be selected upon consultation and approval of the program director.

Fundamentals of Artificial Intelligence Post Baccalaureate Certificate

  • This course provides you with the necessary mathematical background to understand algorithms encountered in machine learning, artificial intelligence, and related fields. Topics covered include probability theory, statistics, calculus, linear algebra, and optimization.

  • This course covers data types, statements, expressions, control flow, top Python core libraries (NumPy, SciPy, Pandas, Matplotlib, and Seaborn) and modeling libraries (Statsmodels and Scikit-learn). Project-based learning is used to help students develop effective problem solving and collaboration skills. Note: This course is for graduate students only. Related Courses: DSCI 303

  • This course provides an introduction to machine learning. Topics include supervised learning, machine learning algorithms, learning theory, reinforcement learning and adaptive control, neural networks, and applications of machine learning to data mining, autonomous navigation, and web data processing. Related Courses: DSCI-408  Prerequisite: DSCI-503

  • This course provides an introduction to the field of Artificial Intelligence. Topics covered may include, but are not limited to, History of Artificial Intelligence, logic, game theory, search algorithms, knowledge representation, and automated planning. Prerequisite: DSCI 503

  • This course introduces you to a range of potential ethical issues related to the current and future use of artificial intelligence. Topics include the role of artificial intelligence in society, as well as the use of artificial intelligence in areas such as manufacturing, finance, healthcare, government, and law enforcement.

Machine Learning Post Baccalaureate Certificate

  • This course covers practical issues in data analysis and graphics such as programming in R, debugging R code, Jupyter Notebook, cloud computing, data exploration, and data visualization. Project-based learning is used to help you develop effective problem solving and collaboration skills. Note: This course is for graduate students only.

  • This course covers data types, statements, expressions, control flow, top Python core libraries (NumPy, SciPy, Pandas, Matplotlib, and Seaborn) and modeling libraries (Statsmodels and Scikit-learn). Project-based learning is used to help students develop effective problem solving and collaboration skills. Note: This course is for graduate students only. Related Courses: DSCI 303

  • This course covers practical issues in relational database systems, such as creating databases, updating data, retrieving data, and saving data in databases. Project-based learning is used to help you develop effective problem solving and collaboration skills. Note: This course is for graduate students only.

  • This course provides an introduction to machine learning. Topics include supervised learning, machine learning algorithms, learning theory, reinforcement learning and adaptive control, neural networks, and applications of machine learning to data mining, autonomous navigation, and web data processing. Related Courses: DSCI-408  Prerequisite: DSCI-503

  • This course introduces you to fundamental statistical learning techniques that can be applied to real-world business problems. Topics include generalized linear models, tree-based models, clustering methods, and principal components analysis. It trains students to understand key steps and considerations in building predictive models, selecting a best model, and effectively communicating the model results. Project-based learning is used to help you develop effective problem solving and collaboration skills. 
    Related Courses: DSCI-412 Prerequisite: DSCI-502

Cybersecurity Incident Response Graduate Certificate

  • This course covers the Controls for Effective Cyber Defense, which are a recommended set of actions that provide specific and actionable ways to deter potential attacks. Discussion will focus on how organizations can use these controls to define the starting point for their defenses, direct their resources on actions with immediate payoff, and focus their attention on additional risk issues that are unique to their business or mission.

  • This course presents the concepts needed to effectively manage information technology resources. It focuses on the role a CIO plays, the planning, scheduling and risk considerations, along with the strategic role that information technology systems play in an organization.

  • This course will cover the principles of cybersecurity incident response and forensics, which include a recommended set of forensic principles to provide specific methods to identify and manage security related events. Discussion will focus on how to leverage practices used to identify and analyze forensic data received from devices and the responsible actions to manage a security incident. You will learn proper cyber defense, evaluation and response methods that are inherent in today’s ever changing technology landscape. Prerequisite: ISYS-600

  • This course explores the laws and policies governments, organizations, and individuals leverage to protect the confidentiality, integrity, and availability of information and technology. This course explores various legal issues that arise in cyberspace, including contracting online, common and tort law, cybercrime, jurisdiction, security and privacy issues and practices, and intellectual property protection. It delves into industry-specific legal, privacy, and ethical considerations in the areas of healthcare, financial reporting, government information, and protecting children online. Lastly, the course provides you with tools for ethical-decision making in a security and privacy context. Prerequisite: ISYS-600

Big Data Post-Baccalaureate Certificate

  • This course covers data types, statements, expressions, control flow, top Python core libraries (NumPy, SciPy, Pandas, Matplotlib, and Seaborn) and modeling libraries (Statsmodels and Scikit-learn). Project-based learning is used to help students develop effective problem solving and collaboration skills. Note: This course is for graduate students only. Related Courses: DSCI 303

  • This course provides an introduction to machine learning. Topics include supervised learning, machine learning algorithms, learning theory, reinforcement learning and adaptive control, neural networks, and applications of machine learning to data mining, autonomous navigation, and web data processing. Related Courses: DSCI-408  Prerequisite: DSCI-503

  • This course covers text analytics, the practice of extracting useful information hidden in unstructured text such as social media, emails, and web pages using Python. Topics include working with corpora, transformations, metadata management, term document matrices, word clouds, and topic models. Project-based learning is used to help students develop effective problem solving and collaboration skills. Related Courses: DSCI-314 Prerequisite: DSCI-508

  • This course targets data scientists and data engineers. It covers programming with RDDs, tuning and debugging Spark applications, Spark SQL, Spark streaming, and machine learning with MLlib. It provides students the tools to quickly tackle big data analysis problems on one machine or hundreds. Project-based learning is used to help students develop effective problem solving and collaboration skills. Related Courses: DSCI-417 Prerequisite: DSCI-508

  • This course is an introduction to deep learning with an emphasis on the development and application of advanced neural networks. It covers convolutional neural networks, recurrent neural networks, generative adversarial networks, and deep reinforcement learning. Project-based learning is used to help students develop effective problem solving and collaboration skills. Related Courses: DSCI-419 Prerequisite: DSCI-508

Project Management Graduate Certificate

  • Course topics include the history of management, perception and communication, motivation theory, leadership and power, group dynamics, conflict management and work design theory.

  • This course focuses on further preparing you to enter the workforce by concentrating on a greater understanding of human relations principles and practices. (Career success is a function of many facets.) The ability to understand and cope effectively with todays work and/or life issues and problems is a skill that is valued by most employers. Many trends, such as workforce diversity, flatter organizations, globalization, teamwork, workplace violence, require a greater understanding of human relations. Prerequisite: MGMT 647

  • This course will examine how to effectively integrate operations across all functional areas of the organization. Prerequisite: MGMT 647

  • This course examines the roles and skills of the project manager and project teams through the phases of the project life cycle. Topics including project initiation and planning, project organizational structure, teamwork, leadership, resource planning and scheduling, control and project termination. Case studies of real organizations focus on the issues associated with new product, reengineering, technology implementation projects, and behavioral aspects including culture, conflict, risk and change management. The course is a general coverage of project management issues commonly found in the project management certification resources relevant for a wide variety of project types. Prerequisite: MGMT 647

Cybersecurity Penetration Testing Graduate Certificate

  • This course covers the Controls for Effective Cyber Defense, which are a recommended set of actions that provide specific and actionable ways to deter potential attacks. Discussion will focus on how organizations can use these controls to define the starting point for their defenses, direct their resources on actions with immediate payoff, and focus their attention on additional risk issues that are unique to their business or mission.

  • This hands-on course applies a penetration testing framework to ethical hacking. Emphasis is placed on penetrating testing methodologies for various types of penetration tests, including Reconnaissance, Social Engineering, and Network Penetration Testing. This course, in conjunction with ISYS-671, prepares you for the EC-Council CEH exam. Prerequisite: ISYS-600

  • This hands-on course applies a penetration testing framework to ethical hacking. Emphasis is placed on penetrating testing methodologies for various types of penetration tests, including hacking web servers, wireless networks, mobile platforms and cloud computing. This course in conjunction with ISYS-670 prepares you for the EC-Council CEH exam. Prerequisite: ISYS 670

Choose 1 of the following courses:

  • This course presents the concepts needed to effectively manage information technology resources. It focuses on the role a CIO plays, the planning, scheduling and risk considerations, along with the strategic role that information technology systems play in an organization.

  • This course familiarizes students with mobile devices and technology used by industry. You will identify and analyze data that can be retrieved from mobile devices, such as cell phones, tablets, smart phones and GPS devices. Prerequisite: ISYS-600

To ensure the best possible educational experience for our students, we may update our curriculum to reflect emerging and changing employer and industry trends.

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Skills, Concepts, or Opportunities Gained with a Master’s Degree in Data Analytics

A typical master’s in data analytics curriculum consists of courses that can give students in-depth knowledge and skills in several aspects of data analytics. Many of these data analytics courses will cover the following skills, concepts, or opportunities:

  • Looking for trends, making decisions, and identifying opportunities. More than ever, businesses and organizations are using large amounts of data to make decisions, increase revenue, and find efficiencies; however, all of that data is meaningless without proper analysis. It is critical that students in this field learn how to look for patterns and trends within the data that can signal opportunities or threats and drive decision-making.
  • Combining operational data with analytical tools. Operational data, which includes data on competitors, suppliers, and finances, can be turned into meaningful information with the right analytical tools. This analysis can, in turn, help improve existing operations.
  • Presenting complex and competitive information. The amount of data at the fingertips of individuals, organizations, and businesses is staggering. As such, it’s critically important for data analytics professionals to be able to present this information in such a way that other stakeholders — company leadership, for example — can understand it. It’s not enough to just analyze the data; people working in data analytics must also be able to effectively communicate their findings.

Common Courses for MS in Data Analytics Students

These are some of the common courses offered for a data analytics degree. Though actual course titles may vary depending on the university, many data analytics programs offer courses that touch on the following concepts:

Data Analytics. The proper use of data, quantitative analysis, and modeling is driving an increasing number of business decisions. All data analytics students need to be comfortable with analyzing different types of data, using different programming languages, and drawing actionable insights from what they discover.

Database Principles. Much of the data that needs to be analyzed is housed in databases. Becoming familiar with database tools and architecture and relevant security issues is essential for data analytics professionals.

Data Visualization. Looking for and finding meaningful insights in large amounts of data is only half of the job — aspiring data analytics professionals must also be able to visualize the data in a meaningful way in order to inform business decision-making. Common forms of data visualization include charts, graphs, and maps.

Forecasting and Predictive Modeling. The field of predictive analytics is growing quickly within data analytics. Businesses often use forecasting and predictive modeling in order to best predict what may happen in the future.

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